{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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xXvBEp+ygq2qw44VB5OdmclafLgqDEIkxNfRoa9kBKg7Al34Pp3w9oY/KFQYh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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x109a17c88>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "%matplotlib inline\n",
    "\n",
    "x = np.linspace(0,20)\n",
    "plt.plot(x, x+0.5)\n",
    "plt.plot(x, 2*x+1, '--')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RangeIndex(start=0, stop=81, step=1)\n",
      "Int64Index([1971, 1972, 1974, 1975, 1976, 1978, 1979, 1982, 1983, 1984, 1985,\n",
      "            1986, 1988, 1989, 1991, 1993, 1994, 1995, 1996, 1997, 1998, 1999,\n",
      "            2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010,\n",
      "            2011, 2012, 2013, 2014],\n",
      "           dtype='int64', name='year')\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "df = pd.read_csv('transcount.csv')\n",
    "print(df.index)\n",
    "df = df.groupby('year').aggregate(np.mean)\n",
    "print(df.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1971 1972 1974 1975 1976 1978 1979 1982 1983 1984 1985 1986 1988 1989\n",
      " 1991 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005\n",
      " 2006 2007 2008 2009 2010 2011 2012 2013 2014]\n"
     ]
    }
   ],
   "source": [
    "years = df.index.values\n",
    "print(years)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2.30000000e+03 3.50000000e+03 4.53333333e+03 3.51000000e+03\n",
      " 7.50000000e+03 1.90000000e+04 4.85000000e+04 9.45000000e+04\n",
      " 8.50000000e+03 2.00000000e+05 1.05333333e+05 2.50000000e+04\n",
      " 2.50000000e+05 7.40117500e+05 6.90000000e+05 3.10000000e+06\n",
      " 5.78977000e+05 5.50000000e+06 4.30000000e+06 8.15000000e+06\n",
      " 7.50000000e+06 1.76000000e+07 3.15000000e+07 4.50000000e+07\n",
      " 1.37500000e+08 1.90066667e+08 3.52000000e+08 1.69000000e+08\n",
      " 6.04000000e+08 3.71600000e+08 9.03200000e+08 3.45000000e+09\n",
      " 1.51166667e+09 1.73350000e+09 2.01482643e+09 5.00000000e+09\n",
      " 4.31000000e+09]\n"
     ]
    }
   ],
   "source": [
    "counts = df['trans_count'].values\n",
    "print(counts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 3.61559210e-01 -7.05783195e+02]\n"
     ]
    }
   ],
   "source": [
    "poly = np.polyfit(years, np.log(counts), deg=1)\n",
    "print(poly)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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DR5R63RRYH+jBZtYL6HXMMceEuVkiEnab18K0m2DNXDiyA/R6Ag7S/914EO7AvxhoYWbN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\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10b521710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.semilogy(years, counts, 'o')\n",
    "plt.semilogy(years, np.exp(np.polyval(poly, years)), label=\"Fit\")\n",
    "plt.legend(loc='upper left')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "       trans_count\n",
      "year              \n",
      "1971  2.300000e+03\n",
      "1972  3.500000e+03\n",
      "1974  4.533333e+03\n",
      "1975  3.510000e+03\n",
      "1976  7.500000e+03\n",
      "1978  1.900000e+04\n",
      "1979  4.850000e+04\n",
      "1982  9.450000e+04\n",
      "1983  8.500000e+03\n",
      "1984  2.000000e+05\n",
      "1985  1.053333e+05\n",
      "1986  2.500000e+04\n",
      "1988  2.500000e+05\n",
      "1989  7.401175e+05\n",
      "1991  6.900000e+05\n",
      "1993  3.100000e+06\n",
      "1994  5.789770e+05\n",
      "1995  5.500000e+06\n",
      "1996  4.300000e+06\n",
      "1997  8.150000e+06\n",
      "1998  7.500000e+06\n",
      "1999  1.760000e+07\n",
      "2000  3.150000e+07\n",
      "2001  4.500000e+07\n",
      "2002  1.375000e+08\n",
      "2003  1.900667e+08\n",
      "2004  3.520000e+08\n",
      "2005  1.690000e+08\n",
      "2006  6.040000e+08\n",
      "2007  3.716000e+08\n",
      "2008  9.032000e+08\n",
      "2009  3.450000e+09\n",
      "2010  1.511667e+09\n",
      "2011  1.733500e+09\n",
      "2012  2.014826e+09\n",
      "2013  5.000000e+09\n",
      "2014  4.310000e+09\n"
     ]
    }
   ],
   "source": [
    "df = pd.read_csv('transcount.csv')\n",
    "df = df.groupby('year').aggregate(np.mean)\n",
    "print(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      gpu_trans_count\n",
      "year                 \n",
      "1997     3.500000e+06\n",
      "1999     1.350000e+07\n",
      "2000     2.500000e+07\n",
      "2001     5.850000e+07\n",
      "2002     8.500000e+07\n",
      "2003     1.260000e+08\n",
      "2004     1.910000e+08\n",
      "2005     3.120000e+08\n",
      "2006     5.325000e+08\n",
      "2007     7.270000e+08\n",
      "2008     1.179500e+09\n",
      "2009     2.154000e+09\n",
      "2010     2.946667e+09\n",
      "2011     4.312712e+09\n",
      "2012     5.310000e+09\n",
      "2013     6.300000e+09\n"
     ]
    }
   ],
   "source": [
    "gpu = pd.read_csv('gpu_transcount.csv')\n",
    "gpu = gpu.groupby('year').aggregate(np.mean)\n",
    "print(gpu)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "       trans_count  gpu_trans_count\n",
      "year                               \n",
      "1971  2.300000e+03              NaN\n",
      "1972  3.500000e+03              NaN\n",
      "1974  4.533333e+03              NaN\n",
      "1975  3.510000e+03              NaN\n",
      "1976  7.500000e+03              NaN\n",
      "1978  1.900000e+04              NaN\n",
      "1979  4.850000e+04              NaN\n",
      "1982  9.450000e+04              NaN\n",
      "1983  8.500000e+03              NaN\n",
      "1984  2.000000e+05              NaN\n",
      "1985  1.053333e+05              NaN\n",
      "1986  2.500000e+04              NaN\n",
      "1988  2.500000e+05              NaN\n",
      "1989  7.401175e+05              NaN\n",
      "1991  6.900000e+05              NaN\n",
      "1993  3.100000e+06              NaN\n",
      "1994  5.789770e+05              NaN\n",
      "1995  5.500000e+06              NaN\n",
      "1996  4.300000e+06              NaN\n",
      "1997  8.150000e+06     3.500000e+06\n",
      "1998  7.500000e+06              NaN\n",
      "1999  1.760000e+07     1.350000e+07\n",
      "2000  3.150000e+07     2.500000e+07\n",
      "2001  4.500000e+07     5.850000e+07\n",
      "2002  1.375000e+08     8.500000e+07\n",
      "2003  1.900667e+08     1.260000e+08\n",
      "2004  3.520000e+08     1.910000e+08\n",
      "2005  1.690000e+08     3.120000e+08\n",
      "2006  6.040000e+08     5.325000e+08\n",
      "2007  3.716000e+08     7.270000e+08\n",
      "2008  9.032000e+08     1.179500e+09\n",
      "2009  3.450000e+09     2.154000e+09\n",
      "2010  1.511667e+09     2.946667e+09\n",
      "2011  1.733500e+09     4.312712e+09\n",
      "2012  2.014826e+09     5.310000e+09\n",
      "2013  5.000000e+09     6.300000e+09\n",
      "2014  4.310000e+09              NaN\n"
     ]
    }
   ],
   "source": [
    "df = pd.merge(df, gpu, how='outer', left_index=True, right_index=True)\n",
    "print(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "       trans_count  gpu_trans_count\n",
      "year                               \n",
      "1971  2.300000e+03     0.000000e+00\n",
      "1972  3.500000e+03     0.000000e+00\n",
      "1974  4.533333e+03     0.000000e+00\n",
      "1975  3.510000e+03     0.000000e+00\n",
      "1976  7.500000e+03     0.000000e+00\n",
      "1978  1.900000e+04     0.000000e+00\n",
      "1979  4.850000e+04     0.000000e+00\n",
      "1982  9.450000e+04     0.000000e+00\n",
      "1983  8.500000e+03     0.000000e+00\n",
      "1984  2.000000e+05     0.000000e+00\n",
      "1985  1.053333e+05     0.000000e+00\n",
      "1986  2.500000e+04     0.000000e+00\n",
      "1988  2.500000e+05     0.000000e+00\n",
      "1989  7.401175e+05     0.000000e+00\n",
      "1991  6.900000e+05     0.000000e+00\n",
      "1993  3.100000e+06     0.000000e+00\n",
      "1994  5.789770e+05     0.000000e+00\n",
      "1995  5.500000e+06     0.000000e+00\n",
      "1996  4.300000e+06     0.000000e+00\n",
      "1997  8.150000e+06     3.500000e+06\n",
      "1998  7.500000e+06     0.000000e+00\n",
      "1999  1.760000e+07     1.350000e+07\n",
      "2000  3.150000e+07     2.500000e+07\n",
      "2001  4.500000e+07     5.850000e+07\n",
      "2002  1.375000e+08     8.500000e+07\n",
      "2003  1.900667e+08     1.260000e+08\n",
      "2004  3.520000e+08     1.910000e+08\n",
      "2005  1.690000e+08     3.120000e+08\n",
      "2006  6.040000e+08     5.325000e+08\n",
      "2007  3.716000e+08     7.270000e+08\n",
      "2008  9.032000e+08     1.179500e+09\n",
      "2009  3.450000e+09     2.154000e+09\n",
      "2010  1.511667e+09     2.946667e+09\n",
      "2011  1.733500e+09     4.312712e+09\n",
      "2012  2.014826e+09     5.310000e+09\n",
      "2013  5.000000e+09     6.300000e+09\n",
      "2014  4.310000e+09     0.000000e+00\n"
     ]
    }
   ],
   "source": [
    "df = df.replace(np.nan, 0)\n",
    "print(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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kGEXVsaNpNyQEunSB2247tfsrIqIuVs5FpD8wFwgEsoD+qrrIyfsICFHVq103apTMIuBZ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\nhngnAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10b8f4198>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "years = df.index.values\n",
    "counts = df['trans_count'].values\n",
    "gpu_counts = df['gpu_trans_count'].values\n",
    "\n",
    "poly = np.polyfit(years, np.log(counts), deg=1)\n",
    "plt.plot(years, np.polyval(poly, years), label=\"Fit\")\n",
    "\n",
    "gpu_start = gpu.index.values.min()\n",
    "y_ann = np.log(df.at[gpu_start, 'trans_count'])\n",
    "ann_str = \"First GPU\\n%d\"%gpu_start\n",
    "plt.annotate(ann_str, xy=(gpu_start, y_ann),\n",
    "             arrowprops=dict(arrowstyle=\"->\"), xytext=(-40, 25), \n",
    "             textcoords=\"offset points\")\n",
    "\n",
    "cnt_log = np.log(counts)\n",
    "plt.scatter(years, cnt_log, c='r',\n",
    "            s=20+200*gpu_counts/gpu_counts.max(), \n",
    "            alpha=0.5, label='Scatter plot')\n",
    "plt.legend(loc='upper left')\n",
    "plt.grid()\n",
    "plt.xlabel(\"Year\")\n",
    "plt.ylabel(\"Log of transistor counts\", fontsize=16)\n",
    "plt.title(\"Moore's Law & Transistor Counts\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x10ba3ba58>"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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tGmMWAYuioqLurGpcStV3L/+wi3mxyTxwWQf+MrwjANm5eexPO8m+tBz2p50k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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10baf5780>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot(logy=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x10b5e0780>"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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vZo7Od14n9qRWAx06dpL9B1/j0DG7kSQVW/CGQWq++/e9yPZdYywZGmJicpIdWzdyzaY1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\nSYVMEJKkQiYISVKh/w8AgXiII0ZxlwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10b857160>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[df['gpu_trans_count']>0].plot(kind='scatter',\n",
    "                                 x='trans_count',\n",
    "                                 y='gpu_trans_count',\n",
    "                                 loglog=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x10b5c4320>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from pandas.plotting import lag_plot\n",
    "lag_plot(np.log(df['trans_count']))\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
